QUIZORA

Quizora is an AI-powered adaptive learning platform designed to help students prepare for Saudi standardized exams and professional certifications through personalized practice questions and intelligent difficulty adjustment using Bayesian Knowledge Tracing (BKT).

The platform focuses on:

Qudurat — General Aptitude Test (Verbal & Quantitative)
Tahseeli — Achievement Test (Chemistry, Biology)
Professional Certifications — CCNA, CompTIA Security+, AWS Cloud Practitioner, PMP

Demo

🌐 Live Application

https://quizora-five.vercel.app Try the platform yourself, including the Policy Scenario Analysis feature.

🎥 Video Walkthrough

Watch on YouTube A guided walkthrough of QUIZORA and the AI Readiness Assessment Assistant.

📚 Knowledge Base

View archive — the 12 authentic policy/standards documents used by the AI Readiness Assessment Assistant.

Features

Adaptive Learning Engine (BKT)

Quizora uses Bayesian Knowledge Tracing to:

Track user mastery levels per skill
Analyze correct/incorrect answers after every response
Adjust question difficulty dynamically (Easy → Medium → Hard)
Personalize each learning session based on accumulated performance

AI Question Generation

The system integrates with OpenAI GPT-4o-mini to:

Generate multiple-choice questions in Arabic and English
Create detailed educational explanations per question
Produce questions across three difficulty levels
Avoid duplicate questions using a 50-question threshold per course

AI Chatbot Assistant

Real-time conversational support during learning sessions
Supports both Arabic and English
Powered by OpenAI GPT-4o-mini via Supabase Edge Functions

Performance Analytics

Detailed reports per course and skill
Visual progress charts
Strength and weakness identification

Policy Scenario Analysis (AI Readiness Assessment)

Lets an institutional user describe an AI-use-case scenario and receive a readiness assessment grounded in a 12-document Knowledge Base (ITU, UNESCO, SDAIA, ETEC, OECD, NDMO)
Mapped to the ITU-T Y.3172 pipeline, with cited policy gaps and recommendations
RAG-based: pgvector semantic search over the Knowledge Base + GPT-4o-mini synthesis via the analyze-scenario Edge Function

Tech Stack

Frontend

React 18
TypeScript
Tailwind CSS + shadcn/ui (Radix UI)
Vite

Backend

Supabase (PostgreSQL + Auth + Edge Functions)
Deno runtime for Edge Functions
JWT-based authentication with Google OAuth support

AI Integration

OpenAI API — GPT-4o-mini

Deployment

Frontend: Vercel
Backend: Supabase Cloud

Project Structure

QUIZORA-main/
│
├── public/                        # Static assets (logo, robots.txt)
│
├── src/
│   ├── App.tsx                    # Root component and route definitions
│   ├── main.tsx                   # App entry point
│   │
│   ├── components/                # All UI pages and components
│   │   ├── LandingPage.tsx        # Public landing/home page
│   │   ├── AuthPage.tsx           # Login and registration
│   │   ├── ResetPasswordPage.tsx  # Password reset flow
│   │   ├── Dashboard.tsx          # User dashboard with stats
│   │   ├── CoursesPage.tsx        # Browse available courses
│   │   ├── CourseDetailPage.tsx   # Course detail and skill breakdown
│   │   ├── QuestionsPage.tsx      # Active quiz/question session
│   │   ├── EvaluationPage.tsx     # Post-quiz evaluation and feedback
│   │   ├── ContinueLearningPage.tsx # Resume previous sessions
│   │   ├── ReportsPage.tsx        # Performance reports and analytics
│   │   ├── ProfilePage.tsx        # User profile management
│   │   ├── SettingsPage.tsx       # App settings (language, theme)
│   │   ├── ScenarioAnalysisPage.tsx # Policy Scenario Analysis UI
│   │   ├── ChatBot.tsx            # Floating AI chatbot widget
│   │   ├── ProtectedRoute.tsx     # Auth guard for protected routes
│   │   │
│   │   ├── layout/
│   │   │   ├── AppLayout.tsx      # Main authenticated layout wrapper
│   │   │   ├── AppSidebar.tsx     # Navigation sidebar
│   │   │   └── AppTopbar.tsx      # Top navigation bar
│   │   │
│   │   └── ui/                    # shadcn/ui reusable components
│   │       └── (button, card, dialog, table, chart, ...)
│   │
│   ├── pages/
│   │   ├── Index.tsx              # Root page redirect
│   │   └── NotFound.tsx           # 404 page
│   │
│   ├── contexts/
│   │   ├── AuthContext.tsx        # Global auth state (user session)
│   │   └── LanguageContext.tsx    # Arabic/English language toggle
│   │
│   ├── hooks/
│   │   ├── useUserStats.ts        # Hook for fetching user stats
│   │   ├── use-toast.ts           # Toast notification hook
│   │   └── use-mobile.tsx         # Responsive breakpoint hook
│   │
│   ├── services/
│   │   └── adaptiveEngine.ts      # Client-side calls to adaptive-engine Edge Function
│   │
│   └── integrations/
│       └── supabase/
│           ├── client.ts          # Supabase client initialization
│           └── types.ts           # Auto-generated database types
│
├── supabase/
│   ├── config.toml                # Supabase project configuration
│   │
│   ├── functions/
│   │   ├── adaptive-engine/       # Main BKT engine: start session, submit answer,
│   │   │   └── index.ts           #   next question, end session, AI question generation
│   │   ├── generate-questions/    # On-demand question generation for a specific skill
│   │   │   └── index.ts
│   │   ├── generate-explanation/  # Generate or regenerate AI explanation for a question
│   │   │   └── index.ts
│   │   ├── review-questions/      # Batch AI review and difficulty correction of questions
│   │   │   └── index.ts
│   │   └── analyze-scenario/      # RAG-based scenario analysis (pgvector retrieval + GPT-4o-mini)
│   │       └── index.ts
│   │
│   └── migrations/
│       ├── ..._seed_initial_data.sql   # Database schema and initial seed data
│       └── ...                         # Additional migrations
│
├── scripts/                       # Knowledge Base ingestion pipeline
│   ├── ingest-knowledge-base.mjs  # Embeds and loads KB chunks into pgvector
│   ├── validate-knowledge-base.mjs # Validates KB chunk structure before ingestion
│   └── verify-ingestion.mjs       # Verifies ingested chunk counts/integrity
│
├── data/
│   └── quizora_kb_chunks.jsonl    # Knowledge Base dataset (719 chunks, 12 documents)
│
├── .env                           # Environment variables (not committed)
├── package.json
├── vite.config.ts
├── tailwind.config.ts
└── tsconfig.json

Prerequisites

Before running the project, make sure you have:

Node.js v18 or higher
npm v9 or higher
A Supabase project — supabase.com
An OpenAI API key — platform.openai.com
Supabase CLI (for deploying Edge Functions) — install via:
```
npm install -g supabase
```

Environment Variables

Create a .env file in the project root:

VITE_SUPABASE_URL=https://your-project.supabase.co
VITE_SUPABASE_ANON_KEY=your_supabase_anon_key
OPENAI_API_KEY=your_openai_api_key

Note: OPENAI_API_KEY is used inside Supabase Edge Functions, not the frontend. Set it as a Supabase secret (see Deployment section).

Running Locally

1. Clone the repository

git clone https://github.com/SajaALfahmi/QUIZORA.git
cd QUIZORA

2. Install dependencies

npm install

3. Set up environment variables

cp .env.example .env
# Then fill in your Supabase URL and anon key

4. Apply database migrations

supabase login
supabase link --project-ref your_project_ref
supabase db push

5. Deploy Edge Functions

supabase functions deploy adaptive-engine
supabase functions deploy generate-questions
supabase functions deploy generate-explanation
supabase functions deploy review-questions
supabase functions deploy analyze-scenario

6. Set Edge Function secrets

supabase secrets set OPENAI_API_KEY=your_openai_api_key

7. Run the development server

npm run dev

The app will be available at http://localhost:5173

Supported Courses

Qudurat (General Aptitude Test)

Sub-category	Description
Verbal	Verbal reasoning questions
Quantitative	Quantitative/numerical reasoning

Tahseeli (Achievement Test)

Sub-category	Description
Chemistry	High school chemistry
Biology	High school biology

Professional Certifications

Certification	Description
CCNA	Cisco Certified Network Associate
CompTIA Security+	Cybersecurity fundamentals
AWS Cloud Practitioner	AWS cloud concepts
PMP	Project Management Professional

Adaptive Difficulty Logic (BKT)

Difficulty is selected based on the user's mastery level, updated after every answer:

Mastery Level	Assigned Difficulty
≥ 0.7	Hard
0.4 – 0.69	Medium
< 0.4	Easy

BKT Parameters used:

Parameter	Value	Description
P_L0	0.3	Initial knowledge probability
P_T	0.15	Learning transition probability
P_G	0.2	Guess probability
P_S	0.1	Slip probability

New questions are AI-generated automatically when the question pool for a course falls below 50 questions, with 25 questions generated per difficulty level.

Deployment

Frontend (Vercel)

Push your code to GitHub
Connect the repository to Vercel
Add the environment variables (VITE_SUPABASE_URL, VITE_SUPABASE_ANON_KEY) in Vercel project settings
Deploy

Backend (Supabase)

Edge Functions are deployed via the Supabase CLI:

supabase functions deploy adaptive-engine
supabase functions deploy generate-questions
supabase functions deploy generate-explanation
supabase functions deploy review-questions
supabase functions deploy analyze-scenario

Set the OpenAI secret:

supabase secrets set OPENAI_API_KEY=your_openai_api_key

Team

Name

-Saja Alfahmi- -Arwa Alroqi- -Ritaj Alharthi- -Lana Alamir-

Project Advisor: Dr.Mohammed Ikram
University: Umm Al-Qura University — Jamoum University College
Department: Computer Science
Academic Year: 2025/2026

Future Improvements

Dynamic course expansion by administrators (without code changes)
Smarter AI cost management via caching and batch scheduling
Mobile application (Android & iOS) with offline support
Advanced predictive analytics dashboard
Multimedia learning materials (videos, diagrams)
Expanded multilingual and accessibility support
Gamification system

License

This project was developed as a Graduation Project (2025-2026) and is licensed under the MIT License.

Name		Name	Last commit message	Last commit date
data		data
public		public
scripts		scripts
src		src
supabase		supabase
.gitignore		.gitignore
KnowledgeBase (compressed).zip		KnowledgeBase (compressed).zip
LICENSE		LICENSE
README.md		README.md
bun.lock		bun.lock
bun.lockb		bun.lockb
components.json		components.json
deno.lock		deno.lock
eslint.config.js		eslint.config.js
index.html		index.html
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
supabase.zip		supabase.zip
tailwind.config.ts		tailwind.config.ts
tsconfig.app.json		tsconfig.app.json
tsconfig.json		tsconfig.json
tsconfig.node.json		tsconfig.node.json
userflow-whisper-main.code-workspace		userflow-whisper-main.code-workspace
vite.config.ts		vite.config.ts

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QUIZORA

Demo

🌐 Live Application

🎥 Video Walkthrough

📚 Knowledge Base

Features

Adaptive Learning Engine (BKT)

AI Question Generation

AI Chatbot Assistant

Performance Analytics

Policy Scenario Analysis (AI Readiness Assessment)

Tech Stack

Frontend

Backend

AI Integration

Deployment

Project Structure

Prerequisites

Environment Variables

Running Locally

1. Clone the repository

2. Install dependencies

3. Set up environment variables

4. Apply database migrations

5. Deploy Edge Functions

6. Set Edge Function secrets

7. Run the development server

Supported Courses

Qudurat (General Aptitude Test)

Tahseeli (Achievement Test)

Professional Certifications

Adaptive Difficulty Logic (BKT)

Deployment

Frontend (Vercel)

Backend (Supabase)

Team

Future Improvements

License

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